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Across the divisions

Sustainability and ESG in the AI Era

AI sits on both sides of the ESG ledger. Training and running models consumes energy and water, while AI is also used to measure emissions, screen suppliers and draft sustainability reports.

The position

Investors and regulators are starting to ask about both. Boards benefit from a single view: what AI the organization runs, what it costs environmentally, and how AI-generated ESG figures are checked before they are published.

What matters

01

The footprint of AI workloads

Model choice, hosting region and usage patterns change the energy profile of AI. A simple register of AI workloads is the starting point for reporting it.

02

AI-produced ESG data

Figures estimated or extracted by AI need the same assurance trail as any other reported number: source, method, reviewer and date.

03

Governance as the G in ESG

AI oversight, accountability and risk management are becoming part of governance disclosures, alongside data protection and cybersecurity.

04

UAE context

The UAE's Net Zero 2050 Strategic Initiative and growing sustainability reporting expectations make AI's footprint and AI-assisted reporting a board-level topic.

Questions

Should AI appear in our ESG report?
Where it is material, yes: as an operational footprint, as a tool used in producing reported data, and as a governance risk the board oversees.
Can AI be used to prepare ESG disclosures?
It can speed up data collection and drafting, provided every figure keeps a documented source and a human reviewer signs off before publication. An AI audit checks exactly that trail.